HyMAGE: Semantic-Aware Dynamic Hypergraph Generation

CaN: A Core-aware Neural Framework for Attributed Hypergraph Generation

Attributed hypergraph generation aims to synthesize higher-order interaction structures together with node attributes, requiring the joint modeling of complex topology and structure--attribute dependencies. Existing methods incorporate attribute information into the generation process, but they remain largely topology-driven, where attributes mainly serve as auxiliary conditions for node selection. This limits their ability to capture the coupling among attribute semantics, structural roles, and member co-occurrence patterns. To address the limitations, we propose CaN, a core-aware neural generation framework for attributed hypergraphs. CaN integrates the hierarchical k-core structure as an explicit generative prior and uses deep neural encoders to model dependencies among multi-dimensional node attributes. It contains a core-aware structural feature allocation module that assigns node- and hyperedge-level structural features under global statistical and feasibility constraints, and a dynamic autoregressive member assignment module that constructs hyperedges based on hyperedge context, attribute embeddings, residual degree states, and core-level constraints. A two-stage optimization strategy further enhances generation quality. Experiments on real-world attributed hypergraphs show the effectiveness of CaN in structural fidelity and structure--attribute consistency.

From binary to general attributes: attributed hypergraph generation with realistic interplay between structure and attributes

In many real-world scenarios, interactions happen in a group-wise manner with multiple entities, and therefore, hypergraphs are a suitable tool to accurately represent such interactions. Hyperedges in real-world hypergraphs are not composed of randomly selected nodes but are instead formed through structured processes. Consequently, various hypergraph generative models have been proposed to explore fundamental mechanisms underlying hyperedge formation. However, most existing hypergraph generative models do not account for node attributes, which can play a significant role in hyperedge formation. As a result, these models fail to reflect the interactions between structure and node attributes. To address the issue above, we propose NoAH, a stochastic hypergraph generative model for attributed hypergraphs. NoAH utilizes the core–fringe node hierarchy to model hyperedge formation as a series of node attachments and determines attachment probabilities based on node attributes. We further introduce NoAHFit, a parameter learning procedure that fits NoAH to a given real-world hypergraph so that generated hypergraphs reproduce structural and attribute-related patterns. Through experiments on nine datasets across four different domains, we show that NoAH with NoAHFit achieves the best overall average rank among the nine evaluated hypergraph generative models when evaluated across six structure–attribute interplay metrics. Moreover, we discuss variants of NoAH for different types of node attributes, including binary, categorical, and continuous attributes. For cases without pre-existing node attributes, we extend NoAH and NoAHFit to jointly learn latent node attributes together with the parameters of NoAH and use the learned attributes for generation.

TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning

TAHB (Text-Attributed Hypergraph Benchmark) is presented, the first public benchmark integrating hypergraph structures and raw textual attributes, and shows that LLM-enhanced textual semantics improve hypergraph learning performance, while structural and textual information jointly provide the best setting for LLM-based prediction.

DeepIDDFS: efficient entity resolution with hybrid embeddings

ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG

Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query. We identify this mismatch as the representation--inference gap. We propose Agentic Context Engineering for Hierarchical GraphRAG (ACE-GraphRAG), an inference-time context policy layer that supplements and adapts the initial context for generation. ACE-GraphRAG formulates context construction as a policy over gap-aware refinement, retrieval branches, and task-conditioned adaptation. Parallel Differential Retrieval acquires supplementary evidence from depth-oriented factual and breadth-oriented semantic branches. These evidence increments are consolidated with the initial context while preserving provenance and abstraction levels. Full-ACE applies the full policy uniformly within each task family, whereas Adaptive-ACE selects task- and topology-specific policies for individual queries. We evaluate ACE-GraphRAG on HotpotQA, 2WikiMultiHopQA, and four UltraDomain subsets across multi-hop QA and query-focused summarization. Full-ACE outperforms the evaluated RAG and GraphRAG baselines across both task families, while Adaptive-ACE further improves multi-hop QA and is preferred over Full-ACE on all four UltraDomain subsets. Ablation and topology analyses support treating context construction as a query- and task-dependent inference policy rather than a fixed procedure.

DHMNN: A Hypergraph Motif-Based Framework for Directed Hyperlink Prediction.

A novel directed hypergraph motif-based neural network (DHMNN) for directed hyperlink prediction, which simultaneously captures higher order structural and connectivity information from the directed hypergraph topology and significantly outperforms state-of-the-art models.